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475 results on '"Yuan, Jiawei"'

3. Assessing the genomic feature of Chinese patients with ampullary adenocarcinoma: potential therapeutic targets

Author

Zhang Dong, Wan Chong, Chen Chen, Li Qi, Li Mengke, Dou Minghui, Yuan Jiawei, Quan Longxi, Liu Hengchao, Jia Liu, and Geng Zhimin

Subjects
Ampullary adenocarcinoma, Somatic, Germline, DDR, Actionable alteration, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Backgrounds Ampullary adenocarcinoma (AMPAC) is a rare malignancy, treated as pancreatic or intestinal cancer based on its histologic subtype. Little is known about the genomic features of Chinese patients with AMPAC. Materials and methods We enrolled 145 Chinese AMPAC patients in our local cohort and performed a compressive somatic and germline genetic testing using a 156 gene panel. Expression of PD-L1 (clone 28 − 8) was also assessed in tumor specimens from 64 patients. Results The frequency of genetic alterations (GAs) in Chinese patients with AMPAC was found to be distinctive, with TP53, KRAS, SMAD4, APC, CTNNB1, ARID1A, and CDKN2A emerged as the most frequently mutated genes. Comparing with Western patients, significant differences were observed in the prevalence of PIK3CA and ARID2. Furthermore, the incidence of MSI-H was lower in the Chinese cohort, with only two patients identified as MSI-H. Conversely, 11 patients (8.27%) had pathogenic/likely pathogenic germline alterations, all of which were in the DNA damage response (DDR) pathway. In our cohort, 34.48% (22/64) of patients exhibited positive PD-L1 expression in tumor cells, and this expression was associated with GAs in CTNNB1 and BLM. Importantly, over three-fourths of Chinese AMPAC patients in our study had at least one actionable GA, with more than one-fifth of them having actionable GAs classified as Level 3. These actionable GAs were primarily involved in the DDR and PI3K pathways. Notably, GAs in the DDR pathway were detected in both Chinese and Western patients, and regardless of their functional impact, these alterations demonstrated enhanced overall survival rates and higher tumor mutational burden (TMB) levels. Conclusion These findings underscore the distinct genomic landscape of Chinese AMPAC patients and highlight the potential for targeted therapies based on the identified GAs.

Published
2024
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22. LEP-CNN: A Lightweight Edge Device Assisted Privacy-preserving CNN Inference Solution for IoT

Author

Tian, Yifan, Yuan, Jiawei, Yu, Shucheng, and Hou, Yantian

Subjects
Computer Science - Cryptography and Security
Abstract

Supporting convolutional neural network (CNN) inference on resource-constrained IoT devices in a timely manner has been an outstanding challenge for emerging smart systems. To mitigate the burden on IoT devices, the prevailing solution is to offload the CNN inference task, which is usually composed of billions of operations, to public cloud. However, the "offloading-to-cloud" solution may cause privacy breach while moving sensitive data to cloud. For privacy protection, the research community has resorted to advanced cryptographic primitives and approximation techniques to support CNN inference on encrypted data. Consequently, these attempts cause impractical computational overhead on IoT devices and degrade the performance of CNNs. Moreover, relying on the remote cloud can cause additional network latency and even make the system dysfunction when network connection is off. We proposes an extremely lightweight edge device assisted private CNN inference solution for IoT devices, namely LEP-CNN. The main design of LEP-CNN is based on a novel online/offline encryption scheme. The decryption of LEP-CNN is pre-computed offline via utilizing the linear property of the most time-consuming operations of CNNs. As a result, LEP-CNN allows IoT devices to securely offload over 99% CNN operations, and edge devices to execute CNN inference on encrypted data as efficient as on plaintext. LEP-CNN also provides an integrity check option to help IoT devices detect error results with a successful rate over 99%. Experiments on AlexNet show that LEP-CNN can speed up the CNN inference for more than 35 times for resource constrained IoT devices. A homomorphic encryption based AlexNet using CryptoNets is implemented to compare with LEP-CNN to demonstrate that LEP-CNN has a better performance than homomorphic encryption based privacy preserving neural networks under time-sensitive scenarios.

Published
2019

31. CPAR: Cloud-Assisted Privacy-preserving Image Annotation with Randomized KD-Forest

Author

Tian, Yifan, Hou, Yantian, and Yuan, Jiawei

Subjects
Computer Science - Cryptography and Security
Abstract

With the explosive growth in the number of pictures taken by smartphones, organizing and searching pictures has become important tasks. To efficiently fulfill these tasks, the key enabler is annotating images with proper keywords, with which keyword-based searching and organizing become available for images. Currently, smartphones usually synchronize photo albums with cloud storage platforms, and have their images annotated with the help of cloud computing. However, the "offloading-to-cloud" solution may cause privacy breach, since photos from smart photos contain various sensitive information. For privacy protection, existing research made effort to support cloud-based image annotation on encrypted images by utilizing cryptographic primitives. Nevertheless, for each annotation, it requires the cloud to perform linear checking on the large-scale encrypted dataset with high computational cost. This paper proposes a cloud-assisted privacy-preserving image annotation with randomized kd-forest, namely CPAR. With CPAR, users are able to automatically assign keywords to their images by leveraging the power of cloud with privacy protected. CPAR proposes a novel privacy-preserving randomized kd-forest structure, which significantly improves the annotation performance compared with existing research. Thorough analysis is carried out to demonstrate the security of CPAR. Experimental evaluation on the well-known IAPR TC-12 dataset validates the efficiency and effectiveness of CPAR., Comment: 13 pages, 13 figures

Published
2018

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